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|    comp.ai    |    Awaiting the gospel from Sarah Connor    |    1,954 messages    |
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|    makc.the.great@gmail.com to m...@reiss.demon.co.uk    |
|    Re: Web page about a new neural network     |
|    05 Mar 07 11:19:02    |
      On Mar 5, 9:49 am, m...@reiss.demon.co.uk wrote:       > > you wrote in page: the task of each output note was to predict the       > > activity of its corresponding input node       >       > > what exactly does this mean, given multiple input nodes?       >       > I'm not sure exactly what you're confused about - so I shall spell       > out the whole thing as carefully as I can.       >       > The input and output layers of nodes are two dimensional arrays       > of the same size. So there is a simple topological mapping from an       > input node to its "corresponding" output node. For example, take the       > node in the bottom left of the input array - the "corresponding"       > output node would be in the bottom left of the output array.       >       > Each output node is given the task of predicting the activity of its       > "corresponding" input node. That is to say it should be active when       > the corresponding input node is active. But it is important to note       > that there is no direct connection between the corresponding input       > and output nodes, so the output has to infer the activity of its       > corresponding input node from the signals it gets from the other       > nodes it is connected to.       >       > I guess some confusion may have been caused by the phrase       > "corresponding input node" which may accidentally imply that it feeds       > an       > input to the output node (which it doesn't). Maybe I should use some       > other expression like "topologically mapped node in the input layer".       >       > Does that answer your question?       >       > M.       >              so what you mean is 2nd layer is made of nodes that should predict       value of non-connected 1st layer nodes in moment T given signals from       connected 1st layer nodes in moment(s) T-1 (-2, -3, ...)?              I can imagine this being useful for noise reduction and otherwise       automatically patching images (or generally, interpolating partial       data), but beyond that - in context of cortex immitation - what else       it is supposed to do?              [ comp.ai is moderated ... your article may take a while to appear. ]              --- SoupGate-Win32 v1.05        * Origin: you cannot sedate... all the things you hate (1:229/2)    |
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